metagen(TE, seTE, studlab, data=NULL, subset=NULL, sm="", level=.settings$level, level.comb=.settings$level.comb, comb.fixed=.settings$comb.fixed, comb.random=.settings$comb.random, hakn=.settings$hakn, method.tau=.settings$method.tau, tau.preset=NULL, TE.tau=NULL, tau.common=.settings$tau.common, prediction=.settings$prediction, level.predict=.settings$level.predict, method.bias=.settings$method.bias, n.e=NULL, n.c=NULL, backtransf=.settings$backtransf,
pscale=1, irscale = 1, irunit = "person-years", title=.settings$title, complab=.settings$complab, outclab="", label.e=.settings$label.e, label.c=.settings$label.c, label.left=.settings$label.left, label.right=.settings$label.right, byvar, bylab, print.byvar=.settings$print.byvar,
byseparator = .settings$byseparator, keepdata=.settings$keepdata, warn=.settings$warn)"RD", "RR", "OR", "ASD",
"HR", "MD", "SMD", or "ROM"."DL", "PM", "REML", "ML", "HS",
"SJ", "HE", or "EB", can be abbreviated."rank", "linreg", or "mm", can
be abbreviated. See function metabiasbacktransf=TRUE
(default), results for sm="OR" are printed as odds ratios
rather than log odds ratios and results for sm="ZCOR" are
printed as correlations rather than Fisher's z transformed
correlations, for example.sm is equal to
"PLOGIT", "PLN", "PRAW", "PAS", or
"PFT". See also metapropsm is equal to "IR",
"IRLN", "IRS", or "IRFT".TE).c("metagen", "meta") with corresponding
print, summary, plot function. The object is a
list containing the following components: For several arguments defaults settings are utilised (assignments
with .settings$). These defaults can be changed using the
settings.meta function.
Internally, both fixed effect and random effects models are
calculated regardless of values choosen for arguments
comb.fixed and comb.random. Accordingly, the estimate
for the random effects model can be extracted from component
TE.random of an object of class "meta" even if
argument comb.random=FALSE. However, all functions in R
package meta will adequately consider the values for
comb.fixed and comb.random. E.g. function
print.meta will not print results for the random
effects model if comb.random=FALSE.
A prediction interval for treatment effect of a new study is
calculated (Higgins et al., 2009) if arguments prediction and
comb.random are TRUE.
R function update.meta can be used to redo the
meta-analysis of an existing metagen object by only specifying
arguments which should be changed.
For the random effects, the method by Hartung and Knapp (2003) is
used to adjust test statistics and confidence intervals if argument
hakn=TRUE.
The DerSimonian-Laird estimate (1986) is used in the random effects
model if method.tau="DL". The iterative Paule-Mandel method
(1982) to estimate the between-study variance is used if argument
method.tau="PM". Internally, R function paulemandel is
called which is based on R function mpaule.default from R package
metRology from S.L.R. Ellison method.tau) are also available:
method.tau="REML")
method.tau="ML")
method.tau="HS")
method.tau="SJ")
method.tau="HE")
method.tau="EB").
For these methods the R function rma.uni of R package metafor
is called internally. See help page of R function rma.uni for
more details on these methods to estimate between-study variance.
Argument pscale can be used to rescale proportions,
e.g. pscale=1000 means that proportions are expressed as
events per 1000 observations. This is useful in situations with
(very) low event probabilities.
Argument irscale can be used to rescale rates,
e.g. irscale=1000 means that rates are expressed as events
per 1000 time units, e.g. person-years. This is useful in situations
with (very) low rates. Argument irunit can be used to specify
the time unit used in individual studies (default:
"person-years"). This information is printed in summaries and forest
plots if argument irscale is not equal to 1.
DerSimonian R & Laird N (1986), Meta-analysis in clinical trials. Controlled Clinical Trials, 7, 177--188.
Higgins JPT, Thompson SG, Spiegelhalter DJ (2009), A re-evaluation of random-effects meta-analysis. Journal of the Royal Statistical Society: Series A, 172, 137--159.
Knapp G & Hartung J (2003), Improved Tests for a Random Effects Meta-regression with a Single Covariate. Statistics in Medicine, 22, 2693--2710, doi: 10.1002/sim.1482 .
Paule RC & Mandel J (1982), Consensus values and weighting factors. Journal of Research of the National Bureau of Standards, 87, 377--385.
Viechtbauer W (2010), Conducting Meta-Analyses in R with the Metafor Package. Journal of Statistical Software, 36, 1--48.
update.meta, metabin, metacont, print.metadata(Fleiss93)
meta1 <- metabin(event.e, n.e, event.c, n.c, data=Fleiss93, sm="RR", method="I")
meta1
#
# Identical results by using the following commands:
#
meta1
metagen(meta1$TE, meta1$seTE, sm="RR")
forest(metagen(meta1$TE, meta1$seTE, sm="RR"))
#
# Meta-analysis with prespecified between-study variance
#
summary(metagen(meta1$TE, meta1$seTE, sm="RR", tau.preset=sqrt(0.1)))
#
# Meta-analysis of survival data:
#
logHR <- log(c(0.95, 1.5))
selogHR <- c(0.25, 0.35)
metagen(logHR, selogHR, sm="HR")
#
# Paule-Mandel method to estimate between-study variance
# Data from Paule & Mandel (1982)
#
average <- c(27.044, 26.022, 26.340, 26.787, 26.796)
variance <- c(0.003, 0.076, 0.464, 0.003, 0.014)
#
summary(metagen(average, sqrt(variance), sm="MD", method.tau="PM"))
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